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By multiplying equations (8) and (9), the weighted hidden values are summed to be the output vector, Y as follows: Y = WX(2) (10) 29 A Lyapunov Theory-Based Neural Network Approach for Face Recognition Figure 3. Feedforward multilayered neural network T where Y = éëêy1, y2 , ym ùûú and each output node has the relationship in (11). u yr (P ) = å X j(2)wr , j , r = 1, m j =1 (11) Two important parameters are associated with each RBF unit. They are center Cj and gaussian width σj. Every center should well represent the corresponding subclass because the classification in RBF NN is mainly measured based on the distances between the input samples and the centers of each subclass.